Digital Software Engineering Lead Analyst- Vice President

Job Overview The Digital Software Engineering Lead Analyst is a strategic technical leader responsible for designing and engineering enterprise‑grade Agentic AI solutions capable of integrating data from multiple heterogeneous systems and operating reliably at scale. You will act as a hands‑on architect, engineer, and partner to cross‑functional teams—including Data Engineering, Architecture, Enterprise Platforms, and Product—to define the technical approach, AI system design, and integration patterns needed to build robust fault‑tolerant AI agents and AI‑driven automation capabilities. This role requires deep technical breadth across machine learning, LLMs, data pipelines, cloud engineering, orchestration, and modern AI frameworks. The solutions you design will enable strategic automation, cognitive decisioning, and dynamic multi‑agent workflows across the organization. Key Responsibilities AI Solution Architecture & Agentic Systems Design and build agentic AI systems, including autonomous agents, multi‑agent orchestration, tool use, and adaptive decision‑making workflows. Architect fault‑tolerant, scalable AI solutions using modern agent frameworks (e.g., Google_ADK, LangGraph, LangChain, OpenAI Assistants, CrewAI, AutoGen, custom orchestrators). Define the end‑to‑end AI system blueprint, including knowledge integration, orchestration, pipelines, observability, governance, and failover strategies. Evaluate and select LLMs, embeddings, vector stores, and middleware best suited for complex enterprise requirements. Data Integration & Pipeline Engineering Partner with engineering teams to aggregate, ingest, and harmonize data from multiple systems, including APIs, databases, internal platforms, and unstructured sources. Design robust data pipelines optimized for LLM workloads (e.g., chunking, metadata design, semantic indexing, retrieval strategies). Implement mechanisms for ensuring data freshness, quality, and fault tolerance across distributed systems. LLM, RAG, and Generative AI Engineering Build advanced Retrieval‑Augmented Generation (RAG) architectures, including hybrid retrieval, query planning, and retrieval optimization. Develop, tune, and deploy applications leveraging major LLMs (OpenAI, Gemini, Claude, Llama, Mistral, HuggingFace ecosystem). Engineer prompts, system instructions, and reusable prompt templates for deterministic AI behavior. Implement safety guardrails, evaluation pipelines, and bias/error mitigation strategies. AI Platform Engineering & Deployment Develop cloud‑native GenAI applications using containerized infrastructure (Kubernetes, OpenShift, Docker). Build and support production‑grade MLOps/AIOps pipelines, including CI/CD, automated testing, monitoring, model versioning, and rollback strategies. Partner with engineering teams to ensure secure, compliant deployment of all AI workloads. Technical Leadership & Collaboration Serve as a technical SME for AI engineering patterns, solution design, and architecture. Mentor mid‑level engineers and analysts, guiding best practices in AI build patterns and engineering quality. Influence product and platform strategy by providing insights on emerging GenAI and agentic technologies. Qualifications Experience 10+ years of experience in software engineering, AI/ML engineering, systems architecture, or related fields. Proven experience designing and deploying enterprise‑grade AI systems in production. Required Technical Skills Core AI/ML & GenAI Expertise Strong foundations in ML, NLP, embeddings, statistics, neural networks, and LLMs. Extensive hands‑on experience with LLMs: Gemini, OpenAI, Claude, Mistral, Llama, open source models, etc. Deep expertise in RAG architectures, including retrieval optimization, vector search, and semantic data modeling. Experience building agentic AI using Google_ADK or LangGraph. Programming & Data Engineering Strong proficiency in Python and libraries such as Pandas, NumPy, scikit‑learn, PyTorch, TensorFlow, Transformers, FastAPI, LangChain, LlamaIndex. Hands‑on experience with vector databases: Pinecone, PGVector, MongoDB Atlas Vector Search, Neo4j, Milvus, etc. Experience building pipelines for large‑scale unstructured data processing. Cloud, DevOps, & MLOps Strong CI/CD experience: GitLab CI, Jenkins, Azure DevOps, ArgoCD, GitHub Actions. Expertise deploying GenAI solutions in production using Kubernetes, Docker, Helm, serverless runtimes, or cloud‑native LLM services. Experience with monitoring, observability, and logging frameworks relevant for AI workloads. Soft Skills Exceptional problem‑solving and analytical skills. Ability to execute independently while operating effectively in ambiguity. Strong collaboration skills across engineering, architecture, and product teams. Deep commitment to ethics, transparency, and responsible AI usage. Preferred Qualifications Experience building AI systems in regulated or enterprise environments. Experience using knowledge graphs, graph databases, or enterprise metadata systems. Familiarity with AIOps, agent monitoring, or AI governance frameworks. Education Bachelor’s degree or equivalent experience required. Master’s degree preferred. Equal Opportunity Employment Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law. If you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity, review Accessibility at Citi. View Citi’s EEO Policy Statement and the Know Your Rights poster. #J-18808-Ljbffr Citigroup Inc.